OpenRaiser/Intern-Atlas huggingface dataset:
GenTT: Generate Vectorized Codes for General Tensor Permutation
Venue: arXiv cs.DS 2025
Year: 2025

Tensor permutation is a fundamental operation widely applied in AI, tensor networks, and related fields. However, it is extremely complex, and different shapes and permutation maps can make a huge difference. SIMD permutation began to be studied in 2006, but the best method at that time was to split complex permutations into multiple simple permutations to do SIMD, which might increase the complexity for very complex permutations. Subsequently, as tensor contraction gained significant attention, researchers explored structured permutations associated with tensor contraction. Progress on general permutations has been limited, and with increasing SIMD bit widths, achieving efficient performance for these permutations has become increasingly challenging. We propose a SIMD permutation toolkit, \system, that generates optimized permutation code for arbitrary instruction sets, bit widths, tensor shapes, and permutation patterns, while maintaining low complexity. In our experiments, \system is able to achieve up to $38\times$ speedup for special cases and $5\times$ for general gases compared to Numpy.
LogSage: An LLM-Based Framework for CI/CD Failure Detection and Remediation with Industrial Validation
Venue: arXiv cs.SE 2025
Year: 2025

Continuous Integration and Deployment (CI/CD) pipelines are critical to modern software engineering, yet diagnosing and resolving their failures remains complex and labor-intensive. We present LogSage, the first end-to-end LLM-powered framework for root cause analysis (RCA) and automated remediation of CI/CD failures. LogSage employs a token-efficient log preprocessing pipeline to filter noise and extract critical errors, then performs structured diagnostic prompting for accurate RCA. For solution generation, it leverages retrieval-augmented generation (RAG) to reuse historical fixes and invokes automation fixes via LLM tool-calling. On a newly curated benchmark of 367 GitHub CI/CD failures, LogSage achieves over 98\% precision, near-perfect recall, and an F1 improvement of more than 38\% points in the RCA stage, compared with recent LLM-based baselines. In a year-long industrial deployment at ByteDance, it processed over 1.07M executions, with end-to-end precision exceeding 80\%. These results demonstrate that LogSage provides a scalable and practical solution for automating CI/CD failure management in real-world DevOps workflows.
Learning-at-Criticality in Large Language Models for Quantum Field Theory and Beyond
Venue: arXiv cs.LG 2025
Year: 2025

Fundamental physics often confronts complex symbolic problems with few guiding exemplars or established principles. While artificial intelligence (AI) offers promise, its typical need for vast datasets to learn from hinders its use in these information-scarce frontiers. We introduce learning at criticality (LaC), a reinforcement learning (RL) scheme that tunes Large Language Models (LLMs) to a sharp learning transition, addressing this information scarcity. At this transition, LLMs achieve peak generalization from minimal data, exemplified by 7-digit base-7 addition -- a test of nontrivial arithmetic reasoning. To elucidate this peak, we analyze a minimal concept-network model (CoNet) designed to capture the essence of how LLMs might link tokens. Trained on a single exemplar, this model also undergoes a sharp learning transition. This transition exhibits hallmarks of a second-order phase transition, notably power-law distributed solution path lengths. At this critical point, the system maximizes a ``critical thinking pattern" crucial for generalization, enabled by the underlying scale-free exploration. This suggests LLMs reach peak performance by operating at criticality, where such explorative dynamics enable the extraction of underlying operational rules. We demonstrate LaC in quantum field theory: an 8B-parameter LLM, tuned to its critical point by LaC using a few exemplars of symbolic Matsubara sums, solves unseen, higher-order problems, significantly outperforming far larger models. LaC thus leverages critical phenomena, a physical principle, to empower AI for complex, data-sparse challenges in fundamental physics.
PlückeRF: A Line-based 3D Representation for Few-view Reconstruction
Venue: arXiv cs.CV 2025
Year: 2025

Feed-forward 3D reconstruction methods aim to predict the 3D structure of a scene directly from input images, providing a faster alternative to per-scene optimization approaches. Significant progress has been made in single-view and few-view reconstruction using learned priors that infer object shape and appearance, even for unobserved regions. However, there is substantial potential to enhance these methods by better leveraging information from multiple views when available. To address this, we propose a few-view reconstruction model that more effectively harnesses multi-view information. Our approach introduces a simple mechanism that connects the 3D representation with pixel rays from the input views, allowing for preferential sharing of information between nearby 3D locations and between 3D locations and nearby pixel rays. We achieve this by defining the 3D representation as a set of structured, feature-augmented lines; the PlückeRF representation. Using this representation, we demonstrate improvements in reconstruction quality over the equivalent triplane representation and state-of-the-art feedforward reconstruction methods.
Verbalized Confidence Triggers Self-Verification: Emergent Behavior Without Explicit Reasoning Supervision
Venue: arXiv cs.CL 2025
Year: 2025

Uncertainty calibration is essential for the safe deployment of large language models (LLMs), particularly when users rely on verbalized confidence estimates. While prior work has focused on classifiers or short-form generation, confidence calibration for chain-of-thought (CoT) reasoning remains largely unexplored. Surprisingly, we find that supervised fine-tuning with scalar confidence labels alone suffices to elicit self-verification behavior of language models, without any explicit reasoning supervision or reinforcement learning-based rewards. Despite being trained only to produce a verbalized confidence score without any self-verifying examples, the model learns to generate longer and self-checking responses for low-confidence queries while providing more concise answers for high-confidence ones. We further propose a simple rethinking method that boosts performance via test-time scaling based on calibrated uncertainty. Experiments on GSM8K and held-out reasoning tasks such as MATH-500 and ARC-Challenge show that our confidence-aware fine-tuning improves both calibration and accuracy, while also enhancing interpretability by aligning the model's reasoning path with its confidence.
HUMOF: Human Motion Forecasting in Interactive Social Scenes
Venue: arXiv cs.CV 2025
Year: 2025

Complex scenes present significant challenges for predicting human behaviour due to the abundance of interaction information, such as human-human and humanenvironment interactions. These factors complicate the analysis and understanding of human behaviour, thereby increasing the uncertainty in forecasting human motions. Existing motion prediction methods thus struggle in these complex scenarios. In this paper, we propose an effective method for human motion forecasting in interactive scenes. To achieve a comprehensive representation of interactions, we design a hierarchical interaction feature representation so that high-level features capture the overall context of the interactions, while low-level features focus on fine-grained details. Besides, we propose a coarse-to-fine interaction reasoning module that leverages both spatial and frequency perspectives to efficiently utilize hierarchical features, thereby enhancing the accuracy of motion predictions. Our method achieves state-of-the-art performance across four public datasets. Code will be released when this paper is published.
Graph Neural Networks for Resource Allocation in Multi-Channel Wireless Networks
Venue: arXiv cs.LG 2025
Year: 2025

As the number of mobile devices continues to grow, interference has become a major bottleneck in improving data rates in wireless networks. Efficient joint channel and power allocation (JCPA) is crucial for managing interference. In this paper, we first propose an enhanced WMMSE (eWMMSE) algorithm to solve the JCPA problem in multi-channel wireless networks. To reduce the computational complexity of iterative optimization, we further introduce JCPGNN-M, a graph neural network-based solution that enables simultaneous multi-channel allocation for each user. We reformulate the problem as a Lagrangian function, which allows us to enforce the total power constraints systematically. Our solution involves combining this Lagrangian framework with GNNs and iteratively updating the Lagrange multipliers and resource allocation scheme. Unlike existing GNN-based methods that limit each user to a single channel, JCPGNN-M supports efficient spectrum reuse and scales well in dense network scenarios. Simulation results show that JCPGNN-M achieves better data rate compared to eWMMSE. Meanwhile, the inference time of JCPGNN-M is much lower than eWMMS, and it can generalize well to larger networks.
Survey of Active Learning Hyperparameters: Insights from a Large-Scale Experimental Grid
Venue: arXiv cs.LG 2025
Year: 2025

Annotating data is a time-consuming and costly task, but it is inherently required for supervised machine learning. Active Learning (AL) is an established method that minimizes human labeling effort by iteratively selecting the most informative unlabeled samples for expert annotation, thereby improving the overall classification performance. Even though AL has been known for decades, AL is still rarely used in real-world applications. As indicated in the two community web surveys among the NLP community about AL, two main reasons continue to hold practitioners back from using AL: first, the complexity of setting AL up, and second, a lack of trust in its effectiveness. We hypothesize that both reasons share the same culprit: the large hyperparameter space of AL. This mostly unexplored hyperparameter space often leads to misleading and irreproducible AL experiment results. In this study, we first compiled a large hyperparameter grid of over 4.6 million hyperparameter combinations, second, recorded the performance of all combinations in the so-far biggest conducted AL study, and third, analyzed the impact of each hyperparameter in the experiment results. In the end, we give recommendations about the influence of each hyperparameter, demonstrate the surprising influence of the concrete AL strategy implementation, and outline an experimental study design for reproducible AL experiments with minimal computational effort, thus contributing to more reproducible and trustworthy AL research in the future.
SLURM Heterogeneous Jobs for Hybrid Classical-Quantum Workflows
Venue: arXiv cs.DC 2025
Year: 2025

A method for efficient scheduling of hybrid classical-quantum workflows is presented, based on standard tools available on common supercomputer systems. Moderate interventions by the user are required, such as splitting a monolithic workflow in to basic building blocks and ensuring the data flow. This bares the potential to significantly reduce idle time of the quantum resource as well as overall wall time of co-scheduled workflows. Relevant pseudo-code samples and scripts are provided to demonstrate the simplicity and working principles of the method.
Animal Pose Labeling Using General-Purpose Point Trackers
Venue: arXiv cs.CV 2025
Year: 2025

Automatically estimating animal poses from videos is important for studying animal behaviors. Existing methods do not perform reliably since they are trained on datasets that are not comprehensive enough to capture all necessary animal behaviors. However, it is very challenging to collect such datasets due to the large variations in animal morphology. In this paper, we propose an animal pose labeling pipeline that follows a different strategy, i.e. test time optimization. Given a video, we fine-tune a lightweight appearance embedding inside a pre-trained general-purpose point tracker on a sparse set of annotated frames. These annotations can be obtained from human labelers or off-the-shelf pose detectors. The fine-tuned model is then applied to the rest of the frames for automatic labeling. Our method achieves state-of-the-art performance at a reasonable annotation cost. We believe our pipeline offers a valuable tool for the automatic quantification of animal behavior. Visit our project webpage at https://zhuoyang-pan.github.io/animal-labeling.
JointSplat: Probabilistic Joint Flow-Depth Optimization for Sparse-View Gaussian Splatting
Venue: arXiv cs.CV 2025
Year: 2025

Reconstructing 3D scenes from sparse viewpoints is a long-standing challenge with wide applications. Recent advances in feed-forward 3D Gaussian sparse-view reconstruction methods provide an efficient solution for real-time novel view synthesis by leveraging geometric priors learned from large-scale multi-view datasets and computing 3D Gaussian centers via back-projection. Despite offering strong geometric cues, both feed-forward multi-view depth estimation and flow-depth joint estimation face key limitations: the former suffers from mislocation and artifact issues in low-texture or repetitive regions, while the latter is prone to local noise and global inconsistency due to unreliable matches when ground-truth flow supervision is unavailable. To overcome this, we propose JointSplat, a unified framework that leverages the complementarity between optical flow and depth via a novel probabilistic optimization mechanism. Specifically, this pixel-level mechanism scales the information fusion between depth and flow based on the matching probability of optical flow during training. Building upon the above mechanism, we further propose a novel multi-view depth-consistency loss to leverage the reliability of supervision while suppressing misleading gradients in uncertain areas. Evaluated on RealEstate10K and ACID, JointSplat consistently outperforms state-of-the-art (SOTA) methods, demonstrating the effectiveness and robustness of our proposed probabilistic joint flow-depth optimization approach for high-fidelity sparse-view 3D reconstruction.
Automated Mechanism to Support Trade Transactions in Smart Contracts with Upgrade and Repair
Venue: arXiv cs.SE 2025
Year: 2025

In our previous research, we addressed the problem of automated transformation of models, represented using the business process model and notation (BPMN) standard, into the methods of a smart contract. The transformation supports BPMN models that contain complex multi-step activities that are supported using our concept of multi-step nested trade transactions, wherein the transactional properties are enforced by a mechanism generated automatically by the transformation process from a BPMN model to a smart contract. In this paper, we present a methodology for repairing a smart contract that cannot be completed due to events that were not anticipated by the developer and thus prevent the completion of the smart contract. The repair process starts with the original BPMN model fragment causing the issue, providing the modeler with the innermost transaction fragment containing the failed activity. The modeler amends the BPMN pattern on the basis of successful completion of previous activities. If repairs exceed the inner transaction's scope, they are addressed using the parent transaction's BPMN model. The amended BPMN model is then transformed into a new smart contract, ensuring consistent data and logic transitions. We previously developed a tool, called TABS+, as a proof of concept (PoC) to transform BPMN models into smart contracts for nested transactions. This paper describes the tool TABS+R, developed by extending the TABS+ tool, to allow the repair of smart contracts.
Pre$^3$: Enabling Deterministic Pushdown Automata for Faster Structured LLM Generation
Venue: arXiv cs.CL 2025
Year: 2025

Extensive LLM applications demand efficient structured generations, particularly for LR(1) grammars, to produce outputs in specified formats (e.g., JSON). Existing methods primarily parse LR(1) grammars into a pushdown automaton (PDA), leading to runtime execution overhead for context-dependent token processing, especially inefficient under large inference batches. To address these issues, we propose Pre$^3$ that exploits deterministic pushdown automata (DPDA) to optimize the constrained LLM decoding efficiency. First, by precomputing prefix-conditioned edges during the preprocessing, Pre$^3$ enables ahead-of-time edge analysis and thus makes parallel transition processing possible. Second, by leveraging the prefix-conditioned edges, Pre$^3$ introduces a novel approach that transforms LR(1) transition graphs into DPDA, eliminating the need for runtime path exploration and achieving edge transitions with minimal overhead. Pre$^3$ can be seamlessly integrated into standard LLM inference frameworks, reducing time per output token (TPOT) by up to 40% and increasing throughput by up to 36% in our experiments. Our code is available at https://github.com/ModelTC/lightllm.
Boosting Open-Source LLMs for Program Repair via Reasoning Transfer and LLM-Guided Reinforcement Learning
Venue: arXiv cs.SE 2025
Year: 2025

Several closed-source LLMs have consistently outperformed open-source alternatives in program repair tasks, primarily due to their superior reasoning capabilities and extensive pre-training. This paper introduces Repairity, a novel three-stage methodology that significantly narrows this performance gap through reasoning extraction and reinforcement learning. Our approach: (1) systematically filters high-quality reasoning traces from closed-source models using correctness verification, (2) transfers this reasoning knowledge to open-source models via supervised fine-tuning, and (3) develops reinforcement learning with LLM-based feedback to further optimize performance. Empirical evaluation across multiple program repair benchmarks demonstrates that Repairity improves the performance of Qwen2.5-Coder-32B-Instruct, a base open source LLM, by 8.68\% on average, reducing the capability gap with Claude-Sonnet3.7, a state-of-the-art closed-source model, from 10.05% to 1.35%. Ablation studies confirm that both reasoning extraction and LLM-guided reinforcement learning contribute significantly to these improvements. Our methodology generalizes effectively to additional code-related tasks, enabling organizations to leverage high-quality program repair capabilities while maintaining the customizability, transparency, and deployment flexibility inherent to open-source models.
Vision Remember: Recovering Visual Information in Efficient LVLM with Vision Feature Resampling
Venue: arXiv cs.CV 2025
Year: 2025

The computational expense of redundant vision tokens in Large Vision-Language Models (LVLMs) has led many existing methods to compress them via a vision projector. However, this compression may lose visual information that is crucial for tasks relying on fine-grained spatial relationships, such as OCR and Chart&Table Understanding. In this paper, we propose to resample original vision features across the LLM decoder layers to recover visual information and attain efficiency. Following this principle, we introduce Vision Remember, which includes two key modules: (1) Token-Feature Cross-Attention Layer and (2) Token Bidirectional Self-Attention Layer. In the Token bidirectional attention, we employ self-attention mechanism to maintain the bidirectional interaction between vision tokens and the text-guided token. In the Token-Feature interaction attention, we introduce local cross-attention to resample the visual feature and utilize the multi-level fusion to enrich the visual representation. We conduct comprehensive experiments on multiple visual understanding benchmarks and the results with the LLaVA-NeXT baseline show that Vision Remember outperforms TokenPacker by +2.7 and FastV by +5.7 across nearly all the settings. Compared with previous vision feature re-fusion methods, our approach also surpasses DeepStack by +3.9 and SVA Aggregator by +3.4 on the same baseline. The experimental results validate the generalization capability of the proposed method when combined with various efficient vision projectors and LVLMs.
Automatic Multi-level Feature Tree Construction for Domain-Specific Reusable Artifacts Management
Venue: arXiv cs.SE 2025
Year: 2025

With the rapid growth of open-source ecosystems (e.g., Linux) and domain-specific software projects (e.g., aerospace), efficient management of reusable artifacts is becoming increasingly crucial for software reuse. The multi-level feature tree enables semantic management based on functionality and supports requirements-driven artifact selection. However, constructing such a tree heavily relies on domain expertise, which is time-consuming and labor-intensive. To address this issue, this paper proposes an automatic multi-level feature tree construction framework named FTBUILDER, which consists of three stages. It automatically crawls domain-specific software repositories and merges their metadata to construct a structured artifact library. It employs clustering algorithms to identify a set of artifacts with common features. It constructs a prompt and uses LLMs to summarize their common features. FTBUILDER recursively applies the identification and summarization stages to construct a multi-level feature tree from the bottom up. To validate FTBUILDER, we conduct experiments from multiple aspects (e.g., tree quality and time cost) using the Linux distribution ecosystem. Specifically, we first simultaneously develop and evaluate 24 alternative solutions in the FTBUILDER. We then construct a three-level feature tree using the best solution among them. Compared to the official feature tree, our tree exhibits higher quality, with a 9% improvement in the silhouette coefficient and an 11% increase in GValue. Furthermore, it can save developers more time in selecting artifacts by 26% and improve the accuracy of artifact recommendations with GPT-4 by 235%. FTBUILDER can be extended to other open-source software communities and domain-specific industrial enterprises.
From Spikes to Speech: NeuroVoc -- A Biologically Plausible Vocoder Framework for Auditory Perception and Cochlear Implant Simulation
Venue: arXiv cs.SD 2025
Year: 2025

We present NeuroVoc, a flexible model-agnostic vocoder framework that reconstructs acoustic waveforms from simulated neural activity patterns using an inverse Fourier transform. The system applies straightforward signal processing to neurogram representations, time-frequency binned outputs from auditory nerve fiber models. Crucially, the model architecture is modular, allowing for easy substitution or modification of the underlying auditory models. This flexibility eliminates the need for speech-coding-strategy-specific vocoder implementations when simulating auditory perception in cochlear implant (CI) users. It also allows direct comparisons between normal hearing (NH) and electrical hearing (EH) models, as demonstrated in this study. The vocoder preserves distinctive features of each model; for example, the NH model retains harmonic structure more faithfully than the EH model. We evaluated perceptual intelligibility in noise using an online Digits-in-Noise (DIN) test, where participants completed three test conditions: one with standard speech, and two with vocoded speech using the NH and EH models. Both the standard DIN test and the EH-vocoded groups were statistically equivalent to clinically reported data for NH and CI listeners. On average, the NH and EH vocoded groups increased SRT compared to the standard test by 2.4 dB and 7.1 dB, respectively. These findings show that, although some degradation occurs, the vocoder can reconstruct intelligible speech under both hearing models and accurately reflects the reduced speech-in-noise performance experienced by CI users.